Systematizing Modeler Experience (MX) in Model-Driven Engineering Success Stories
June 28, 2024 Β· Declared Dead Β· π Journal of Software and Systems Modeling
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Reyhaneh Kalantari, Julian Oertel, Joeri Exelmans, Satrio Adi Rukmono, Vasco Amaral, Matthias Tichy, Katharina Juhnke, Jan-Philipp SteghΓΆfer, Silvia AbrahΓ£o
arXiv ID
2406.20035
Category
cs.SE: Software Engineering
Citations
3
Venue
Journal of Software and Systems Modeling
Last Checked
4 months ago
Abstract
Modeling is often associated with complex and heavy tooling, leading to a negative perception among practitioners. However, alternative paradigms, such as everything-as-code or low-code, are gaining acceptance due to their perceived ease of use. This paper explores the dichotomy between these perceptions through the lens of ``modeler experience'' (MX). MX includes factors such as user experience, motivation, integration, collaboration \& versioning and language complexity. We examine the relationships between these factors and their impact on different modeling usage scenarios. Our findings highlight the importance of considering MX when understanding how developers interact with modeling tools and the complexities of modeling and associated tooling.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted